Exemplar Based

Exemplar-based methods leverage example data to guide various machine learning tasks, aiming to improve accuracy, efficiency, or controllability. Current research focuses on refining these methods across diverse applications, including image and video colorization, action counting, and class-incremental learning, often employing attention mechanisms, generative models (like StyleGANs and VAEs), and transformer architectures to enhance performance. This approach is significant because it allows for improved results in scenarios with limited labeled data or the need for user-specified semantic control, impacting fields ranging from media restoration to human-computer interaction. A key trend is the exploration of non-exemplar approaches, seeking to achieve comparable performance without relying on storing example data.